{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四周作业基础作业\n",
    "1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。 \n",
    "2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。 \n",
    "3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。 \n",
    "4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。 \n",
    "5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats\n",
    "from datetime import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "\n",
    "#显示前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null int64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null int64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null int64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "对于连续性特征可以使用sns.distplot函数进行展示其分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(df[\"MEDV\"], bins=30, kde=True)\n",
    "plt.xlabel(\"Median value of owner-occupied homes\", fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。\n",
    "可以使用corr()函数得到矩阵中两两特征的相关系数。 并使用hearmap画出相关系数热图 DataFrame的corr()方法先计算出每对特征间的相关矩阵。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x293bb9e7a48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#get the names of all the columns\n",
    "cols = df.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 得到相关系数的绝对值，通常认为相关系数的绝对值大于0.5的特征为强相关\n",
    "data_corr = data_corr.abs()\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features,突出重要信息\n",
    "sns.heatmap(data_corr, mask=data_corr < 0.5, cbar=False)\n",
    "\n",
    "#plt.savefig(\"house_coor.png\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RAD and TAX = 0.91\n",
      "NOX and DIS = 0.77\n",
      "INDUS and NOX = 0.76\n",
      "AGE and DIS = 0.75\n",
      "LSTAT and MEDV = 0.74\n",
      "NOX and AGE = 0.73\n",
      "INDUS and TAX = 0.72\n",
      "INDUS and DIS = 0.71\n",
      "RM and MEDV = 0.70\n",
      "NOX and TAX = 0.67\n",
      "ZN and DIS = 0.66\n",
      "INDUS and AGE = 0.64\n",
      "CRIM and RAD = 0.63\n",
      "RM and LSTAT = 0.61\n",
      "NOX and RAD = 0.61\n",
      "INDUS and LSTAT = 0.60\n",
      "AGE and LSTAT = 0.60\n",
      "INDUS and RAD = 0.60\n",
      "NOX and LSTAT = 0.59\n",
      "CRIM and TAX = 0.58\n",
      "ZN and AGE = 0.57\n",
      "TAX and LSTAT = 0.54\n",
      "DIS and TAX = 0.53\n",
      "ZN and INDUS = 0.53\n",
      "ZN and NOX = 0.52\n",
      "AGE and TAX = 0.51\n",
      "PTRATIO and MEDV = 0.51\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "如果特征之间高度相关，可考虑进行PCA降维（特征层面）或加正则项（模型层面）\n",
    "\n",
    "如果两个特征的相关性特别强，比如绝对值高于0.9，则可以考虑只保留其中一个特征以达到降维的目的。\n",
    "\n",
    "或者在选用模型时加入L1正则项以在训练模型的时候自动淘汰一些特征。\n",
    "\n",
    "因为在使用最小二乘求解的时候，需要对特征矩阵求逆，而能够求逆的条件是特征矩阵的行或列之间是线性无关的情况。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.989737</td>\n",
       "      <td>0.063466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.994276</td>\n",
       "      <td>0.033389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.099338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO         B     LSTAT  \n",
       "0  0.208015      0.3  1.000000  0.089680  \n",
       "1  0.104962      0.5  1.000000  0.204470  \n",
       "2  0.104962      0.5  0.989737  0.063466  \n",
       "3  0.066794      0.6  0.994276  0.033389  \n",
       "4  0.066794      0.6  1.000000  0.099338  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "\n",
    "# 从原始数据中分离输入特征x和输出y\n",
    "y = df['MEDV']\n",
    "X = df.drop('MEDV', axis = 1)\n",
    "\n",
    "# 尝试对y（房屋价格）做log变换，对log变换后的价格进行估计\n",
    "log_y = np.log1p(y)\n",
    "\n",
    "feat_names = X.columns\n",
    "log_y = np.log1p(y)\n",
    "X[\"RAD\"].astype(\"object\")\n",
    "X_cat = X[\"RAD\"]\n",
    "X_cat = pd.get_dummies(X_cat, prefix=\"RAD\")\n",
    "X= X.drop(\"RAD\", axis = 1)\n",
    "X = scaler.fit_transform(X)# 对训练数据，先调用fit方法训练模型，得到模型参数；然后对训练数据和测试数据进行transform\n",
    "\n",
    "fe_data = pd.DataFrame(data = X, columns = feat_names.drop(\"RAD\"), index = df.index)#  feat_names.drop(\"RAD\")\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>...</th>\n",
       "      <th>RAD_24</th>\n",
       "      <th>RAD_1</th>\n",
       "      <th>RAD_2</th>\n",
       "      <th>RAD_3</th>\n",
       "      <th>RAD_4</th>\n",
       "      <th>RAD_5</th>\n",
       "      <th>RAD_6</th>\n",
       "      <th>RAD_7</th>\n",
       "      <th>RAD_8</th>\n",
       "      <th>RAD_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.067815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.577505</td>\n",
       "      <td>0.641607</td>\n",
       "      <td>0.269203</td>\n",
       "      <td>0.208015</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.547998</td>\n",
       "      <td>0.782698</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.242302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.694386</td>\n",
       "      <td>0.599382</td>\n",
       "      <td>0.348962</td>\n",
       "      <td>0.104962</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.658555</td>\n",
       "      <td>0.441813</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.063050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.687105</td>\n",
       "      <td>0.528321</td>\n",
       "      <td>0.448545</td>\n",
       "      <td>0.066794</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CRIM    ZN     INDUS  CHAS       NOX        RM       AGE       DIS  \\\n",
       "0  0.000000  0.18  0.067815   0.0  0.314815  0.577505  0.641607  0.269203   \n",
       "1  0.000236  0.00  0.242302   0.0  0.172840  0.547998  0.782698  0.348962   \n",
       "2  0.000236  0.00  0.242302   0.0  0.172840  0.694386  0.599382  0.348962   \n",
       "3  0.000293  0.00  0.063050   0.0  0.150206  0.658555  0.441813  0.448545   \n",
       "4  0.000705  0.00  0.063050   0.0  0.150206  0.687105  0.528321  0.448545   \n",
       "\n",
       "        TAX  PTRATIO  ...  RAD_24  RAD_1  RAD_2  RAD_3  RAD_4  RAD_5  RAD_6  \\\n",
       "0  0.208015      0.3  ...       0      1      0      0      0      0      0   \n",
       "1  0.104962      0.5  ...       0      0      1      0      0      0      0   \n",
       "2  0.104962      0.5  ...       0      0      1      0      0      0      0   \n",
       "3  0.066794      0.6  ...       0      0      0      1      0      0      0   \n",
       "4  0.066794      0.6  ...       0      0      0      1      0      0      0   \n",
       "\n",
       "   RAD_7  RAD_8  RAD_24  \n",
       "0      0      0       0  \n",
       "1      0      0       0  \n",
       "2      0      0       0  \n",
       "3      0      0       0  \n",
       "4      0      0       0  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fe_data = pd.concat([fe_data, X_cat], axis = 1, ignore_index=False)\n",
    "fe_data[\"log_MEDV\"] = log_y\n",
    "fe_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 39)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_MinMax=fe_data.drop(\"log_MEDV\",axis=1)\n",
    "y=fe_data['log_MEDV']\n",
    "X_train, X_test, y_train, y_test = train_test_split(x_MinMax, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on train is 0.8112010417433881\n",
      "The r2 score of LinearRegression on test is 0.7083054590265425\n"
     ]
    }
   ],
   "source": [
    "# 线性回归\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "reg=LinearRegression()# 实例化机器学习模型\n",
    "reg_fit=reg.fit(X_train,y_train)# 用训练数据训练模型参数\n",
    "# 用训练好的模型对测试集进行预测\n",
    "y_test_pred_lr = reg.predict(X_test)\n",
    "y_train_pred_lr = reg.predict(X_train)\n",
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "# 训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))\n",
    "# 测试集\n",
    "print( 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of Ridge on train is 0.8111212589789354\n",
      "The r2 score of Ridge on test is 0.709500207649691\n"
     ]
    }
   ],
   "source": [
    "# 岭回归,L2正则\n",
    "from sklearn.linear_model import RidgeCV\n",
    "rid=RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1])\n",
    "rid_fit=rid.fit(X_train,y_train)\n",
    "y_test_pred_rid = rid.predict(X_test)\n",
    "y_train_pred_rid = rid.predict(X_train)\n",
    "print ('The r2 score of Ridge on train is', r2_score(y_train, y_train_pred_rid))\n",
    "print( 'The r2 score of Ridge on test is', r2_score(y_test, y_test_pred_rid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on train is 0.8111501169710051\n",
      "The r2 score of LassoCV on test is 0.7084753377414171\n"
     ]
    }
   ],
   "source": [
    "# Lasso回归，L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "las=LassoCV(cv=5, random_state=0)\n",
    "las_fit=las.fit(X_train,y_train)\n",
    "y_test_pred_las = las.predict(X_test)\n",
    "y_train_pred_las = las.predict(X_train)\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_las))\n",
    "print( 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_las))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 4)\n",
      "alpha is: 0.1\n"
     ]
    }
   ],
   "source": [
    "# 岭回归,L2正则\n",
    "from sklearn.linear_model import RidgeCV\n",
    "alphas=[1e-3, 1e-2, 1e-1, 1]\n",
    "rid=RidgeCV(alphas=alphas,store_cv_values=True)# 只有当cv=Ｎｏｎｅ时,store_cv_values才能设置为Ｔｒｕｅ\n",
    "rid_fit=rid.fit(X_train,y_train)\n",
    "y_test_pred_rid = rid.predict(X_test)\n",
    "y_train_pred_rid = rid.predict(X_train)\n",
    "cv_result=rid.cv_values_# 在实例化学习模型时，要加上store_cv_values=True，才能使用cv_values_属性。\n",
    "print(cv_result.shape)# 每一行代表每一个样本的mse值，每一列代表每一个对应的alpha所得到的结果。\n",
    "cv_result[:5]\n",
    "print ('alpha is:', rid.alpha_)# rid.alpha_为模型得到的最优参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图展示最好的alpha值是不是在mse_mean最小的时候取得的\n",
    "mse_mean = np.mean(rid.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "# 可以看出当mse_mean最小时，alpha等于0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([7.52394784e-02, 7.01685893e-02, 6.54394613e-02, 6.10290606e-02,\n",
       "       5.69159062e-02, 5.30799646e-02, 4.95025526e-02, 4.61662463e-02,\n",
       "       4.30547958e-02, 4.01530466e-02, 3.74468657e-02, 3.49230723e-02,\n",
       "       3.25693741e-02, 3.03743073e-02, 2.83271806e-02, 2.64180235e-02,\n",
       "       2.46375371e-02, 2.29770495e-02, 2.14284733e-02, 1.99842659e-02,\n",
       "       1.86373933e-02, 1.73812954e-02, 1.62098542e-02, 1.51173643e-02,\n",
       "       1.40985045e-02, 1.31483125e-02, 1.22621603e-02, 1.14357317e-02,\n",
       "       1.06650017e-02, 9.94621624e-03, 9.27587456e-03, 8.65071166e-03,\n",
       "       8.06768265e-03, 7.52394784e-03, 7.01685893e-03, 6.54394613e-03,\n",
       "       6.10290606e-03, 5.69159062e-03, 5.30799646e-03, 4.95025526e-03,\n",
       "       4.61662463e-03, 4.30547958e-03, 4.01530466e-03, 3.74468657e-03,\n",
       "       3.49230723e-03, 3.25693741e-03, 3.03743073e-03, 2.83271806e-03,\n",
       "       2.64180235e-03, 2.46375371e-03, 2.29770495e-03, 2.14284733e-03,\n",
       "       1.99842659e-03, 1.86373933e-03, 1.73812954e-03, 1.62098542e-03,\n",
       "       1.51173643e-03, 1.40985045e-03, 1.31483125e-03, 1.22621603e-03,\n",
       "       1.14357317e-03, 1.06650017e-03, 9.94621624e-04, 9.27587456e-04,\n",
       "       8.65071166e-04, 8.06768265e-04, 7.52394784e-04, 7.01685893e-04,\n",
       "       6.54394613e-04, 6.10290606e-04, 5.69159062e-04, 5.30799646e-04,\n",
       "       4.95025526e-04, 4.61662463e-04, 4.30547958e-04, 4.01530466e-04,\n",
       "       3.74468657e-04, 3.49230723e-04, 3.25693741e-04, 3.03743073e-04,\n",
       "       2.83271806e-04, 2.64180235e-04, 2.46375371e-04, 2.29770495e-04,\n",
       "       2.14284733e-04, 1.99842659e-04, 1.86373933e-04, 1.73812954e-04,\n",
       "       1.62098542e-04, 1.51173643e-04, 1.40985045e-04, 1.31483125e-04,\n",
       "       1.22621603e-04, 1.14357317e-04, 1.06650017e-04, 9.94621624e-05,\n",
       "       9.27587456e-05, 8.65071166e-05, 8.06768265e-05, 7.52394784e-05])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso回归，L1正则\n",
    "from sklearn.linear_model import LassoCV\n",
    "las=LassoCV(cv=5, random_state=0)\n",
    "las_fit=las.fit(X_train,y_train)\n",
    "y_test_pred_las = las.predict(X_test)\n",
    "y_train_pred_las = las.predict(X_train)\n",
    "las_mse=las.mse_path_\n",
    "print(las_mse.shape)# LassoCV默认的n_alphas参数是100，其结果表示每一个alpha值进行五次交叉验证得到的mse值。\n",
    "las.alphas_ # 随机选择的100个alpha值（即正则参数），默认值eps=1e-3， means that alpha_min / alpha_max = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "最小二乘： 适用与特征之间相关性不强的数据\n",
    "\n",
    "岭回归： 适用与特征之间有一些相关性的数据\n",
    "\n",
    "Lasso回归： 适用与特征之前有很多强相关性的数据\n",
    "\n",
    "最小二乘线性回归中，目标函数只考虑了模型对训练样本的拟合程度，容易过拟合。\n",
    "岭回归使得线性回归系数收缩，模型稳定。当输入特征之间存在共线性时使用较好。\n",
    "lasso也会收缩回归系数。当正则参数取合适值时，使得有些线性回归系数为0，得到稀疏模型。\n",
    "当输入特征多，有些特征与目标变量之间相关性很弱时，可能只选择强相关的特征，模型解释性好。\n",
    "通过模型结果来看，不同模型中各个变量的比重不同。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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